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2 - Global precipitation estimation from satellite imagery using artificial neural networks

Published online by Cambridge University Press:  15 December 2009

By
S. Sorooshian ,
K.-L. Hsu ,
B. Imam  and
Y. Hong
Edited by
Howard Wheater ,
Soroosh Sorooshian  and
K. D. Sharma
S. Sorooshian
Affiliation:
Professor Department of Civil and Environmental Engineering, University of California, Irvine, California, USA
K.-L. Hsu
Affiliation:
Department of Civil and Environmental Engineering, University of California, Irvine, California, USA
B. Imam
Affiliation:
Department of Civil and Environmental Engineering, University of California, Irvine, California, USA
Y. Hong
Affiliation:
Department of Civil and Environmental Engineering, University of California, Irvine, California, USA
Howard Wheater
Affiliation:
Imperial College of Science, Technology and Medicine, London
Soroosh Sorooshian
Affiliation:
University of California, Irvine
K. D. Sharma
Affiliation:
National Institute of Hydrology, India
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Summary

INTRODUCTION

Precipitation is the key hydrologic variable linking the atmosphere with land-surface processes, and playing a dominant role in both weather and climate. The Global Water and Energy Cycle Experiment (GEWEX), recognizing the strategic role of precipitation data in improving climate research, strongly emphasized the need to achieve global measurement of precipitation with sufficient accuracy to enable the investigation of regional to global water and energy distribution. Additionally, many other international research programs have also placed high priority on the development of reliable global precipitation observation.

During the past few decades, satellite-sensor technology has facilitated the development of innovative approaches to global precipitation observations. Clearly, satellite-based technologies have the potential to provide improved precipitation estimates for large portions of the world where gauge observations are limited. Recently many satellite-based precipitation algorithms have been developed (Ba and Gruber, 2001; Huffman et al., 2002; Joyce et al., 2004; Negri et al., 2002; Sorooshian et al., 2000; Tapiador 2002; Turk et al., 2002; Vicente et al., 1998; Weng et al., 2003). These algorithms generate precipitation products consisting of higher spatial and temporal resolution with potential to be used in hydrologic research and water-resources applications. Evaluation of recently developed precipitation products over various regions is ongoing (Ebert, 2004; Kidd, 2004; Janowiak, 2004).

In this chapter, we will introduce one near-global precipitation product generated from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) algorithm.

Type
Chapter
Information
Hydrological Modelling in Arid and Semi-Arid Areas , pp. 21 - 28
Publisher: Cambridge University Press
Print publication year: 2007

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References

Ba, M. B. and Gruber, A. (2001). GOES multispectral rainfall algorithm (GMSRA), J. Appl. Meteorol., 40, 1500–14.2.0.CO;2>CrossRefGoogle Scholar
Ebert, B. (2004). Monitoring the quality of operational and semi-operational satellite precipitation estimates: the IPWG validation/intercomparison study. 2nd IPWG Working Group Meeting, Naval Research Laboratory, Monterey, CA, USA, 25–28, Oct, 2004.Google Scholar
Ferraro, R. R. and Marks, G. F. (1995). The development of SSM/I rain-rate retrieval algorithms using ground-based radar measurements. J. Atmos. Oceanic Technol., 12, 755–770.2.0.CO;2>CrossRefGoogle Scholar
Guevara, J. M. (2002). Precipitation estimation over Mexico applying PERSIANN system and gauge data.Masters Thesis, University of Arizona.Google Scholar
Gochis, D. J., Shuttleworth, W. J., and Yang, Z.-L. (2002). Sensitivity of the modeled North American Monsoon regional climate to convective parameterization. Monthly Weather Rev., 130, 1282–98.2.0.CO;2>CrossRefGoogle Scholar
Huffman, G. J., Adler, R. F., Stocker, E. F., Bolvin, D. T., and Nelkin, E. J. (2002). A TRMM-based system for real-time quasi-global merged precipitation estimates. TRMM International Science Conference, Honolulu, 22–26 July 2002Google Scholar
Hong, Y., Hsu, K.Sorooshian, S., and Gao, X. (2005). Improved representation of diurnal variability of rainfall retrieval from TRMM-adjusted PERSIANN system, J. Geophys. Res., 110, D06102.Google Scholar
Hong, Y., Hsu, K., Gao, X., and Sorooshian, S. (2004). Precipitation estimation from remotely sensed imagery using artificial neural network – cloud classification system, J. Appl. Meteorol., 43 (12), 1834–53CrossRefGoogle Scholar
Hsu, K., Gao, X., Sorooshian, S., and Gupta, H. V. (1997). Precipitation estimation from remotely sensed information using artificial neural networks. J. Appl. Meteorol., 36, 1176–90.2.0.CO;2>CrossRefGoogle Scholar
Hsu, K., Gupta, H. V., Gao, X., and Sorooshian, S. (1999). A neural network for estimating physical variables from multi-channel remotely sensed imagery: application to rainfall estimation. Water Resour. Res., 35, 1605–18.CrossRefGoogle Scholar
Janowiak, J. E., Joyce, R. J., and Yarosh, Y. (2000). A real-time global half-hourly pixel resolution infrared dataset and its applications, Bull. Am. Meteorol. Soc., 82, 205–17.2.3.CO;2>CrossRefGoogle Scholar
Janowiak, J. (2004). Validation of satellite-derived rainfall estimates and numerical model forecasts of precipitation over the US. 2nd IPWG Working Group Meeting, Naval Research Laboratory, Monterey, CA, USA, 25–28, Oct., 2004.Google Scholar
Joyce, R. J., Janowiak, J. E., Arkin, P. A., and Xie, P. (2004). CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeorol., 5, 487–503.2.0.CO;2>CrossRefGoogle Scholar
Kidd, C. (2004) Validation of satellite rainfall estimates over the mid-latitudes. 2nd IPWG Working Group Meeting, Naval Research Laboratory, Monterey, CA, USA, 25–28, Oct, 2004.Google Scholar
Li, J., Gao, X., Maddox, R. A.Sorooshian, S., and Hsu, K. (2003). Summer weather simulation for the semi-arid lower Colorado River basin: case tests. Monthly Weather Rev., 131 (3), 521–41.2.0.CO;2>CrossRefGoogle Scholar
Negri, A. J., Xu, L., and Adler, R. F. (2002). A TRMM-calibrated infrared rainfall algorithm applied over Brazil. J. Geophys. Res., 107 (D20), 8048–62.CrossRefGoogle Scholar
Sorooshian, S., Hsu, K.Gao, X., Gupta, H. V., Imam, B., and Braithwaite, D. (2000). Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull. Am. Meteorol. Soc., 81, 2035–46.2.3.CO;2>CrossRefGoogle Scholar
Sorooshian, S., Gao, X., Hsu, K.et al. (2002). Diurnal variability of tropical rainfall retrieved from combined GOES and TRMM satellite information, J. Clim., 15, 983–1001.2.0.CO;2>CrossRefGoogle Scholar
Tapiador, F. J. (2002). A new algorithm to generate global rainfall rates from satellite infrared imagery. Revista de Teledeteccion, 18, 57–61.Google Scholar
Turk, J., Ebert, E., Oh, H.-J., Sohn, B.-J., Levizzani, V., Smith, E., and Ferraro, R. (2002). Verification of an operational global precipitation analysis at short time scales. 1st Intl. Precipitation Working Group (IPWG) Workshop, Madrid, Spain, 23–27 September 2002.Google Scholar
Vicente, G. A., Scofield, R. A., and Menzel, W. P. (1998). The operational GOES infrared rainfall estimation technique. Bull. Am. Meteorol. Soc., 79, 1883–98.2.0.CO;2>CrossRefGoogle Scholar
Weng, F. W., Zhao, L., Ferraro, R., Pre, G., Li, X., and Grody, N. C. (2003). Advanced Microwave Sounding Unit (AMSU) cloud and precipitation algorithms, Radio Sci., 38 (4), 8068–79.CrossRefGoogle Scholar
Xu, J., Gao, X. and Sorooshian, S. (2004). Investigate the impact of assimilating satellite rainfall estimates on rainstrom forecast over Southwest United States, Geophysical Research Letters, 31.Google Scholar
Yi, H. (2002). Assimilation of satellite-derived precipitation into the Regional Atmospheric Modeling System (RAMS) and its impact on the weather and hydrology in the southwest United States. Ph. D. Dissertation, Department of Hydrology and Water Resources, University of Arizona.Google Scholar
Yucel, I., Shuttleworth, W. J., Pinker, R. T., Lu, L., and Sorooshian, S. (2002). Impact of ingesting satellite-derived cloud cover into the regional atmospheric modeling system, Monthly Weather Rev., 130, 610–28.2.0.CO;2>CrossRefGoogle Scholar

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  • Global precipitation estimation from satellite imagery using artificial neural networks
    • By S. Sorooshian, Professor Department of Civil and Environmental Engineering, University of California, Irvine, California, USA, K.-L. Hsu, Department of Civil and Environmental Engineering, University of California, Irvine, California, USA, B. Imam, Department of Civil and Environmental Engineering, University of California, Irvine, California, USA, Y. Hong, Department of Civil and Environmental Engineering, University of California, Irvine, California, USA
  • Edited by Howard Wheater, Imperial College of Science, Technology and Medicine, London, Soroosh Sorooshian, University of California, Irvine, K. D. Sharma
  • Book: Hydrological Modelling in Arid and Semi-Arid Areas
  • Online publication: 15 December 2009
  • Chapter DOI: https://doi.org/10.1017/CBO9780511535734.003
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  • Global precipitation estimation from satellite imagery using artificial neural networks
    • By S. Sorooshian, Professor Department of Civil and Environmental Engineering, University of California, Irvine, California, USA, K.-L. Hsu, Department of Civil and Environmental Engineering, University of California, Irvine, California, USA, B. Imam, Department of Civil and Environmental Engineering, University of California, Irvine, California, USA, Y. Hong, Department of Civil and Environmental Engineering, University of California, Irvine, California, USA
  • Edited by Howard Wheater, Imperial College of Science, Technology and Medicine, London, Soroosh Sorooshian, University of California, Irvine, K. D. Sharma
  • Book: Hydrological Modelling in Arid and Semi-Arid Areas
  • Online publication: 15 December 2009
  • Chapter DOI: https://doi.org/10.1017/CBO9780511535734.003
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Global precipitation estimation from satellite imagery using artificial neural networks
    • By S. Sorooshian, Professor Department of Civil and Environmental Engineering, University of California, Irvine, California, USA, K.-L. Hsu, Department of Civil and Environmental Engineering, University of California, Irvine, California, USA, B. Imam, Department of Civil and Environmental Engineering, University of California, Irvine, California, USA, Y. Hong, Department of Civil and Environmental Engineering, University of California, Irvine, California, USA
  • Edited by Howard Wheater, Imperial College of Science, Technology and Medicine, London, Soroosh Sorooshian, University of California, Irvine, K. D. Sharma
  • Book: Hydrological Modelling in Arid and Semi-Arid Areas
  • Online publication: 15 December 2009
  • Chapter DOI: https://doi.org/10.1017/CBO9780511535734.003
Available formats
×